1. Field of Invention
The present invention relates to traffic safety and, more particularly, to a pedestrian detector.
2. Related Prior Art
Drivers have to pay attention to obstacles and above all pedestrians. For traffic safety, it is getting more and more attention to equip a vehicle with a pedestrian detector.
Conventionally, a pedestrian is converted to various templates. The templates are made to simulate a pedestrian at various angles in various poses.
For example, as disclosed by D. M. Gavrila, “Multi-cue Pedestrian Detection and Tracking from a Moving Vehicle,” International Journal of Computer Vision, 2007, D. M. Gavrila, “Pedestrian Detection from a Moving Vehicle,” in Proceedings of the European Conference on Computer Vision (ECCV), 2000, Cheng-Yi Liu, and Li-Chen Fu, “Computer Vision Based Object Detection and Recognition for Vehicle Driving,” IEEE International Conference on Robotics and Automation, 2001, silhouettes or edge images are used to simulate the pedestrian. All of the silhouettes or edge images are converted to distance transform (DT) images.
As disclosed by M. Oren, C. Papageorgiou, P. Sinha, E. Osuna, and T. Pogio. “Pedestrian detection using wavelet templates,” Proceedings of IEEE Conference on Computer Vision and Pattern Recognition, 1997, to solve problems related to translation, scale and orientation of the pedestrian, Harr vertical and horizontal wavelets are used to calculate wavelet coefficients for simulating image characteristics.
As disclosed by Q. Zhu, S. Avidan, M. C. Yeh, and K. T. Cheng, “Fast Human Detection Using a Cascade of Histograms of Oriented Gradients,” IEEE Conference on Computer Vision and Pattern Recognition, 2006 and N. Dalal and B. Triggs, “Histograms of Oriented Gradients for Human Detection,” IEEE Conference on Computer Vision and Pattern Recognition, 2005, histograms of oriented gradients are used to simulate the image characteristics. Via artificial intelligence based on a supported vector machine (“SVM”), a resultant classifier is used to effectively represent the image characteristics and detect the pedestrian.
As disclosed in N. Dalal and B. Triggs, “Histograms of Oriented Gradients for Human Detection,” IEEE Conference on Computer Vision and Pattern Recognition, 2005, effects of using various characteristics such as HOG, Haar wavelets, PCA-SIFT and shape contexts to detect the pedestrian are analyzed and discussed. HOG has been proven to be the best measure to overcome the problems related to the variations in the look of the pedestrian. With HOG, an image is divided into segments, and the magnitudes of gradients in various orientations in the segments are summed up, and a histogram is produced.
As based on the calculation of the gradients, HOG is good in describing the edges of the obstacles. Moreover, as based on statics, HOG tolerates considerable translation and orientation of the edges. However, as based on statics, HOG is poor in providing information related to textures. For example, it is difficult for HOG to tell a long line segment from a plurality of scattered short line segments. Hence, misjudge could occur with HOG where a pedestrian is amid a disorderly environment.
As discussed above, a driver can use various apparatuses to try to detect a pedestrian. The apparatuses are however dissatisfactory.
The present invention is therefore intended to obviate or at least alleviate the problems encountered in prior art.